How accurate can genetic predictions be?

Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

Impact of Privacy Issues on Successful Implementation of Personalized Medicare System

Personalized medicare systems is an emerging field of research, which bears the potential to significantly reduce healthcare expenditures and treatment errors and thereby to revolutionize the entire treatment procedure. In this novel approach, genomic variation in different individuals is duly taken into consideration. However, there exist several serious issues (e.g. privacy concerns) that provide hindrance to large-scale adoption of this medicare system. The main objective of this study has been to identify the privacy issues and to evaluate their impact on successful implementation of this novel medical treatment. The methodology used is empirical and is based on a survey-based post facto procedure. The data collected from the survey are analyzed by using the method of structural modelling analysis. This is an original study in the realm of healthcare management, which reveals that the technology related factors and privacy concerns have considerable impact on the successful implementation of personalized medicare system on a large scale. But the privacy concerns have no significant moderating effect on the impact of technology related factors, so far, the success of implementation of personalized medicine is concerned.

The SickKids Genome Clinic: developing and evaluating a pediatric model for individualized genomic medicine

Our increasing knowledge of how genomic variants affect human health and the falling costs of whole-genome sequencing are driving the development of individualized genomic medicine. This new clinical paradigm uses knowledge of an individual's genomic variants to anticipate, diagnose and manage disease. While individualized genetic medicine offers the promise of transformative change in health care, it forces us to reconsider existing ethical, scientific and clinical paradigms. The potential benefits of pre-symptomatic identification of at-risk individuals, improved diagnostics, individualized therapy, accurate prognosis and avoidance of adverse drug reactions coexist with the potential risks of uninterpretable results, psychological harm, outmoded counseling models and increased health care costs. Here we review the challenges, opportunities and limits of integrating genomic analysis into pediatric clinical practice and describe a model for implementing individualized genomic medicine. Our multidisciplinary team of bioinformaticians, health economists, health services and policy researchers, ethicists, geneticists, genetic counselors and clinicians has designed a 'Genome Clinic' research project that addresses multiple challenges in pediatric genomic medicine--ranging from development of bioinformatics tools for the clinical assessment of genomic variants and the discovery of disease genes to health policy inquiries, assessment of clinical care models, patient preference and the ethics of consent.

Critical Challenges for Adopting Personalized Medicine System in Healthcare Management

Personalized Medicine is an emerging approach in today's healthcare management. It bears a very strong potential to consolidate modern e-health systems fundamentally. Scientists have already discovered some of the personalized drugs that can shift the whole medicare process into a new dimension. However, bringing the change in healthcare management is an easy task. There are several critical challenges in the implementation of Personalized Medicine systems. This paper aims at identifying some of these critical challenges through a survey with medical doctors and patients. Challenges involved in implementing Personalized Medicine are listed. A questionnaire was distributed amongst a set of medical doctors, medical researchers, practitioners in pharmaceutical industries, regulatory board members, and a larger section of patients. The response data collected thereby were analysed statistically by using t-test. Summary of the descriptive statistical results of the responses received from medical doctors and patients are presented in tabular form. Based upon the statistical analysis, an attempt has been made in the paper to make a ranking of the challenges. A comparison of the perspectives of the doctors and patients has been made by using bar diagrams. The observations have been discussed in detail and some specific conclusions have been made. To the best of the author's knowledge and belief, this is the first academic paper in which an attempt has been made to suggest the crucial challenges for the implementation of Personalized Medicine. The study shows that both the medical doctors and patients perceive that genomic analysis of all the individuals is the most critical challenge.

Genome-wide association analysis and genomic prediction of Mycobacterium avium subspecies paratuberculosis infection in US Jersey cattle

Paratuberculosis (Johne’s disease), an enteric disorder in ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), causes economic losses in excess of $200 million annually to the US dairy industry. To identify genomic regions underlying susceptibility to MAP infection in Jersey cattle, a case-control genome-wide association study (GWAS) was performed. Blood and fecal samples were collected from ∼5,000 mature cows in 30 commercial Jersey herds from across the US. Discovery data consisted of 450 cases and 439 controls genotyped with the Illumina BovineSNP50 BeadChip. Cases were animals with positive ELISA and fecal culture (FC) results. Controls were animals negative to both ELISA and FC tests that matched cases on birth date and herd. Validation data consisted of 180 animals including 90 cases (positive to FC) and 90 controls (negative to ELISA and FC), selected from discovery herds and genotyped by Illumina BovineLD BeadChip (∼7K SNPs). Two analytical approaches were used: single-marker GWAS using the GRAMMAR-GC method and Bayesian variable selection (Bayes C) using GenSel software. GRAMMAR-GC identified one SNP on BTA7 at 68 megabases (Mb) surpassing a significance threshold of 5×10⁻⁵. ARS-BFGL-NGS-11887 on BTA23 (27.7 Mb) accounted for the highest percentage of genetic variance (3.3%) in the Bayes C analysis. SNPs identified in common by GRAMMAR-GC and Bayes C in both discovery and combined data were mapped to BTA23 (27, 29 and 44 Mb), 3 (100, 101, 106 and 107 Mb) and 17 (57 Mb). Correspondence between results of GRAMMAR-GC and Bayes C was high (70–80% of most significant SNPs in common). These SNPs could potentially be associated with causal variants underlying susceptibility to MAP infection in Jersey cattle. Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).

Health coaching and genomics-potential avenues to elicit behavior change in those at risk for chronic disease: protocol for personalized medicine effectiveness study in air force primary care

Background:

Type 2 diabetes (T2D) and coronary heart disease (CHD) are prevalent chronic diseases from which military personnel are not exempt. While many genetic markers for these diseases have been identified, the clinical utility of genetic risk testing for multifactorial diseases such as these has not been established. The need for a behavioral intervention such as health coaching following a risk counseling intervention for T2D or CHD also has not been explored. Here we present the rationale, design, and protocol for evaluating the clinical utility of genetic risk testing and health coaching for active duty US Air Force (AF) retirees and beneficiaries.

Primary Study Objectives:

Determine the direct and interactive effects of health coaching and providing genetic risk information when added to standard risk counseling for CHD and T2D on health behaviors and clinical risk markers.

Design:

Four-group (2 X 2 factorial) randomized controlled trial.

Setting:

Two AF primary care clinical settings on the west coast of the United States.

Participants:

Adult AF primary care patients.

Intervention:

All participants will have a risk counseling visit with a clinic provider to discuss personal risk factors for T2D and CHD. Half of the participants (two groups) will also learn of their genetic risk testing results for T2D and CHD in this risk counseling session. Participants randomized to the two groups receiving health coaching will then receive telephonic health coaching over 6 months.

Main Outcome Measures:

Behavioral measures (self-reported dietary intake, physical activity, smoking cessation, medication adherence); clinical outcomes (AF composite fitness scores, weight, waist circumference, blood pressure, fasting glucose, lipids, T2D/CHD risk scores) and psychosocial measures (self-efficacy, worry, perceived risk) will be collected at baseline and 6 weeks, and 3, 6, and 12 months.

Conclusion:

This study tests novel strategies deployed within existing AF primary care to increase adherence to evidence-based diet, physical activity, smoking cessation, and medication recommendations for CHD and T2D risk reduction through methods of patient engagement and self-management support.

Clinical utility of genetic risk testing in primary care: the example of Type 2 diabetes

Genetic advances in Type 2 diabetes (T2D) have led to the discovery and validation of multiple markers for this complex disease. Despite low predictive value of current T2D markers beyond clinical risk factors and family history, researchers are exploring the clinical utility and outcomes of implementation in practice, and testing is available via direct-to-consumer markets. Clinical utility research demonstrates high hypothetical utility to patients for motivating behavior change and potentially reducing risk. However, trials to date have not demonstrated improvements in behavioral and clinical outcomes over and above counseling based on traditional risk factors. Ongoing research in T2D genetics and associated risk-prediction models is necessary to refine genetic risk pathways, algorithms for risk prediction and use of this information in clinical care. Further research is also needed to explore care models and support interventions that address the needs of personalized risk information and sustainable preventive behaviors to reduce the rising prevalence of T2D.